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\usepackage[2331114]{easymcm}  % 载入 EasyMCM 模板文件
\problem{Y}  % 请在此处填写题号
% \usepackage{mathptmx}  % 这是 Times 字体，中规中矩 
\usepackage{palatino}  % mathpazo 这palatino是 COMAP 官方杂志采用的更好看的 Palatino 字体，可替代以上的 mathptmx 宏包

\usepackage{pdfpages}
\usepackage{longtable} %长表格包
\usepackage{tabu}
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\usepackage{listings} %代码排版包
\usepackage{paralist} 
\usepackage{makecell}
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%\usepackage{fourier} %使用花里胡哨符号的宏包
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%    \usepackage{minted}  %代码排版包
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\usepackage{matlab-prettifier} %MATLAB代码排版包
\usepackage{tcolorbox} %美丽盒子包
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%    \usepackage{matlab-prettifier} %MATLAB代码排版包
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\usepackage{pgfornament} %花样装饰包(待研究)
\usepackage[ruled,linesnumbered]{algorithm2e} %伪代码排版包
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\input{new_command.tex}
%\newcommand{\upcite}[1]{\textsuperscript{\textsuperscript{\cite{#1}}}} %
\title{ Used Sailboat Price Evaluation based on \\Mixed Prediction Model and Multiple Linear Regression}  % 标题

% 如需要修改题头（默认为 MCM/ICM），请使用以下命令（此处修改为 MCM）
%\renewcommand{\contest}{MCM}
 %文档开始
\begin{document}

% 此处填写摘要内容
\begin{abstract}
    The price of a commodity often reflects the local economic level
    and environmental indicators. As a typical luxury product, the
    study of regional price effects on \textbf{used sailboats} can not only
    provide reference and guidance for sailboat enthusiasts and
    sailboat brokers, but also provide a detailed description of
    the local economic level and environmental indi-cators.
    In our study, we modeled the influence of sailboat parameters
    and region on listing prices separately.
    
    Several models are established: Model I: Fusion prediction model;
    Model II: A multiple linear regression-based model of regional
    impact indicators; Model III: Joint bp neural net-work and multiple
    linear regression model of regional effects in Hong Kong, etc.

    Before the model was built, we processed the data and found that
    the original dataset had three missing values. Next, we collected 
    9 parameters of the given sailboat for model building.

    For Model I: We first used the PCA method to downscale the 12 used
    sailboat parameter metrics to 6, at which point the cumulative
    contribution rate had reached 85\%.
    Then, we brought the dimensionality reduction data into the SCA
    optimized RF, BP and CNN models, and the fit superiority was 0.80, 
    0.83, and 0.78. We fuse the three models and propose a fu-sion model, 
    and found that the fit superiority could reach 0.88. Meanwhile, the prediction 
    error range was about 7\%-14\%, and the prediction results were relatively 
    stable. The spe-cific fusion process is shown in Equation 5 and the pseudo 
    code.

    For Model II: For the effect of region on listing price, we first selected 
    four regional in-dicators and conducted a multiple regression analysis to 
    derive the effect of each indicator on sailing price. And for the regional 
    effect of sailing variants we proposed a calculation method of regional 
    effect indicators based on ranking changes, which can better describe the
    regional effect of sailing prices. Finally, the actual and statistical 
    significance of the region-al effects are described. The calculation of 
    regional effect indicators is shown in Figure 7.


    For Model III: We first used Pearson correlation analysis to obtain a subset 
    of sailboat data with a correlation coefficient greater than 0.9 with the 
    Hong Kong region. Then we proposed a method to construct a neural network 
    as shown in Figure 9, then brought into the BP neural network for training 
    to obtain the listed prices of sailboats of that type. After this we 
    constructed a multiple linear regression following the second question and 
    derived the regional effect of Hong Kong on monohull and catamaran sailboats. 
    Finally, We substitute the data of both into each other to obtain the 
    consistency between monohull and catamaran sailing boats.

    In addition, our analysis led to some meaningful and interesting conclusions. 
    For ex-ample, some of them (e.g. Solona) alternate between sales and 
    non-sales effects.
    
    %Finally, we perform a sensitivity analysis of the fusion model and for the 
    %forecast of sailboat prices in Hong Kong. The input was changed to 0.95-1.05 
    %of the original input to observe the o2102199.pdfutput. The results are shown in Figure 
    %18 and its caption.

    % 美赛论文中无需注明关键字。若您一定要使用，
    % 请将以下两行的注释号 '%' 去除，以使其生效
    \vspace{5pt}  %mm	毫米	1 mm = 2.845 pt   pt 点	1 pt = 0.351 mm
    \textbf{Keywords}: Fighting Wildfires; Multi-Objective Optimization; Poisson Distribution; Tabu Search Algorithm; Sensitivity Analysis

\end{abstract}

\maketitle  % 生成 Summary Sheet

\tableofcontents  % 生成目录

\input{part_1_pre}
\input{part_2_model}
\input{part_3_Conclusion.tex}
%\input{Memo.tex}
\newpage
\input{part_4_appendix.tex}
\begin{align}
\end{align}
\end{document}  % 结束